Digital Signal Processing, Second Edition enables electrical engineers and technicians in the fields of biomedical, computer, and electronics engineering to master the essential fundamentals of DSP principles and practice. Many instructive worked examples are used to illustrate the material, and the use of mathematics is minimized for easier grasp of concepts. As such, this title is also useful to undergraduates in electrical engineering, and as a reference for science students and practicing engineers.
The book goes beyond DSP theory, to show implementation of algorithms in hardware and software. Additional topics covered include adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as PCM, u-law, ADPCM, and multi-rate DSP and over-sampling ADC.
New to this edition:
This book is targeted to meet the needs of electrical engineers and technicians who design and build hardware and software for DSP systems.
This textbook can also be used in an introductory DSP course at the junior level in undergraduate electrical engineering program at traditional colleges. Additionally, the book should be useful as a reference for undergraduate engineering students, science students, and practicing engineers
Table of contentsChapter 1. Introduction to Digital Signal Processing
1.1 Basic Concepts of Digital Signal Processing
1.2 Basic Digital Signal Processing Examples in Block Diagrams
1.3 Overview of Typical Digital Signal Processing in Real-World Applications
1.4 Digital Signal Processing Applications
Chapter 2. Signal Sampling and Quantization
2.1 Sampling of Continuous Signal
2.2 Signal Reconstruction
2.3 Analog-to-Digital Conversion, Digital-to-Analog Conversion, and Quantization
2.5 MATLAB Programs
Chapter 3. Digital Signals and Systems
3.1 Digital Signals
3.2 Linear Time-Invariant, Causal Systems
3.3 Difference Equations and Impulse Responses
3.4 Bounded-In and Bounded-Out Stability
3.5 Digital Convolution
Chapter 4. Discrete Fourier Transform and Signal Spectrum
4.1 Discrete Fourier Transform
4.2 Amplitude Spectrum and Power Spectrum
4.3 Spectral Estimation Using Window Functions
4.4 Application to Signal Spectral Estimation
4.5 Fast Fourier Transform
Chapter 5. The z-Transform
5.2 Properties of the z-Transform
5.3 Inverse z-Transform
5.4 Solution of Difference Equations Using the z-Transform
Chapter 6. Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations
6.1 The Difference Equation and Digital Filtering
6.2 Difference Equation and Transfer Function
6.3 The z-Plane Pole-Zero Plot and Stability
6.4 Digital Filter Frequency Response
6.5 Basic Types of Filtering
6.6 Realization of Digital Filters
6.7 Application: Signal Enhancement and Filtering
Chapter 7. Finite Impulse Response Filter Design
7.1 Finite Impulse Response Filter Format
7.2 Fourier Transform Design
7.3 Window Method
7.4 Applications: Noise Reduction and Two-Band Digital Crossover
7.5 Frequency Sampling Design Method
7.6 Optimal Design Method
7.7 Realization Structures of Finite Impulse Response Filters
7.8 Coefficient Accuracy Effects on Finite Impulse Response Filters
7.9 Summary of FIR Design Procedures and Selection of FIR Filter Design Methods in Practice
7.11 MATLAB Programs
Chapter 8. Infinite Impulse Response Filter Design
8.1 Infinite Impulse Response Filter Format
8.2 Bilinear Transformation Design Method
8.3 Digital Butterworth and Chebyshev Filter Designs
8.4 Higher-Order Infinite Impulse Response Filter Design Using the Cascade Method
8.5 Application: Digital Audio Equalizer
8.6 Impulse-Invariant Design Method
8.7 Pole-Zero Placement Method for Simple Infinite Impulse Response Filters
8.8 Realization Structures of Infinite Impulse Response Filters
8.9 Application: 60-Hz Hum Eliminator and Heart Rate Detection Using Electrocardiography
8.10 Coefficient Accuracy Effects on Infinite Impulse Response Filters
8.11 Application: Generation and Detection of DTMF Tones Using the Goertzel Algorithm
8.12 Summary of Infinite Impulse Response (IIR) Design Procedures and Selection of the IIR Filter Design Methods in Practice
Chapter 9. Hardware and Software for Digital Signal Processors
9.1 Digital Signal Processor Architecture
9.2 Digital Signal Processor Hardware Units
9.3 Digital Signal Processors and Manufacturers
9.4 Fixed-Point and Floating-Point Formats
9.5 Finite Impulse Response and Infinite Impulse Response Filter Implementations in Fixed-Point Systems
9.6 Digital Signal Processing Programming Examples
Chapter 10. Adaptive Filters and Applications
10.1 Introduction to Least Mean Square Adaptive Finite Impulse Response Filters
10.2 Basic Wiener Filter Theory and Least Mean Square Algorithm
10.3 Applications: Noise Cancellation, System Modeling, and Line Enhancement
10.4 Other Application Examples
10.5 Laboratory Examples Using the TMS320C6713 DSK
Chapter 11. Waveform Quantization and Compression
11.1 Linear Midtread Quantization
11.2 μ-law Companding
11.3 Examples of Differential Pulse Code Modulation (DPCM), Delta Modulation, and Adaptive DPCM G.721
11.4 Discrete Cosine Transform, Modified Discrete Cosine Transform, and Transform Coding in MPEG Audio
11.5 Laboratory Examples of Signal Quantization Using the TMS320C6713 DSK
11.7 MATLAB Programs
Chapter 12. Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and Undersampling of Bandpass Signals
12.1 Multirate Digital Signal Processing Basics
12.2 Polyphase Filter Structure and Implementation
12.3 Oversampling of Analog-to-Digital Conversion
12.4 Application Example: CD Player
12.5 Undersampling of Bandpass Signals
12.6 Sampling Rate Conversion Using the TMS320C6713 DSK
Chapter 13. Subband- and Wavelet-Based Coding
13.1 Subband Coding Basics
13.2 Subband Decomposition and Two-Channel Perfect Reconstruction Quadrature Mirror Filter Bank
13.3 Subband Coding of Signals
13.4 Wavelet Basics and Families of Wavelets
13.5 Multiresolution Equations
13.6 Discrete Wavelet Transform
13.7 Wavelet Transform Coding of Signals
13.8 MATLAB Programs
Chapter 14. Image Processing Basics
14.1 Image Processing Notation and Data Formats
14.2 Image Histogram and Equalization
14.3 Image Level Adjustment and Contrast
14.4 Image Filtering Enhancement
14.5 Image Pseudo-Color Generation and Detection
14.6 Image Spectra
14.7 Image Compression by Discrete Cosine Transform
14.8 Creating a Video Sequence by Mixing Two Images
14.9 Video Signal Basics
14.10 Motion Estimation in Video
Appendix A. Introduction to the MATLAB Environment
A.1 Basic Commands and Syntax
A.2 MATLAB Arrays and Indexing
A.3 Plot Utilities: subplot, plot, stem, and stair
A.4 MATLAB Script Files
A.5 MATLAB Functions
Appendix B. Review of Analog Signal Processing Basics
B.1 Fourier Series and Fourier Transform
B.2 Laplace Transform
B.3 Poles, Zeros, Stability, Convolution, and Sinusoidal Steady-State Response
Appendix C. Normalized Butterworth and Chebyshev Functions
C.1 Normalized Butterworth Function
C.2 Normalized Chebyshev Function
Appendix D. Sinusoidal Steady-State Response of Digital Filters
D.1 Sinusoidal Steady-State Response
Appendix E. Finite Impulse Response Filter Design Equations by the Frequency Sampling Design Method
Appendix F. Wavelet Analysis and Synthesis Equations
F.1 Basic Properties
F.2 Analysis Equations
F.2 Wavelet Synthesis Equations
Appendix G. Some Useful Mathematical Formulas
Appendix 8. Answers to Selected Problems
